Update README.md

README.md

@@ -1,14 +1,62 @@
----
-title: Mcma Space
-emoji: 🚀
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.1.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: AI Malware Analysis
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+# -----------------------------------------------------------------------------
+# MODEL CARD METADATA
+# This YAML block tells Hugging Face how to categorize and display your model.
+# Reference: https://huggingface.co/docs/hub/models-cards
+# -----------------------------------------------------------------------------
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- pytorch
+- text-generation
+- custom-model
+# pipeline_tag: Defines the widget on the right (e.g., text-generation, image-classification)
+pipeline_tag: text-generation
+# inference: true enables the widget. Set to false if you want to disable it.
+inference: true
+---
+
+# Model Card for zeltera/mcma
+
+## Model Description
+
+**zeltera/mcma** is a machine learning model hosted on the Hugging Face Hub. Based on the file structure in the repository, this appears to be a **Transformers-compatible** model (PyTorch/Safetensors).
+
+*   **Developed by:** Zeltera
+*   **Model type:** Pre-trained / Fine-tuned Transformer
+*   **Language(s):** English
+*   **License:** Apache 2.0 (or specify your license)
+*   **Repository:** [zeltera/mcma](https://huggingface.co/zeltera/mcma)
+
+## Intended Uses & Limitations
+
+### Intended Use
+This model is designed for tasks such as:
+*   Text generation
+*   Feature extraction
+*   *(Update this list based on the specific capabilities of your model)*
+
+### Limitations
+*   The model may output biased or inaccurate information.
+*   Performance depends on the quality of the input prompts.
+
+## How to Use
+
+You can use this model directly with the Hugging Face `transformers` library.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "zeltera/mcma"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example usage
+input_text = "Once upon a time"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))